Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	44,184
2	South Dakota	38,949
3	Louisiana	38,670
4	Mississippi	37,404
5	Alabama	35,594
6	Florida	35,403
7	Iowa	34,614
8	Tennessee	33,648
9	Arkansas	33,282
10	Wisconsin	32,271
11	South Carolina	32,142
12	Arizona	32,002
13	Georgia	30,906
14	Idaho	30,862
15	Nebraska	30,711
16	Texas	30,329
17	Utah	30,144
18	Nevada	29,764
19	Illinois	28,105
20	Oklahoma	27,684
21	Rhode Island	27,043
22	Missouri	26,866
23	Kansas	25,703
24	New York	25,250
25	New Jersey	25,216
26	Delaware	23,953
27	North Carolina	23,756
28	District of Columbia	23,301
29	Indiana	22,739
30	Maryland	22,698
31	Montana	22,643
32	California	22,451
33	Minnesota	22,268
34	Massachusetts	21,104
35	Kentucky	20,532
36	Virginia	19,653
37	Puerto Rico	18,362
38	Connecticut	18,078
39	New Mexico	18,073
40	Alaska	16,880
41	Michigan	16,616
42	Wyoming	16,459
43	Ohio	15,881
44	Colorado	15,275
45	Pennsylvania	14,840
46	Washington	13,637
47	West Virginia	11,449
48	Hawaii	10,101
49	Oregon	9,519
50	New Hampshire	7,228
51	Maine	4,455
52	Vermont	3,134

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	North Dakota	1,051
2	Wisconsin	701
3	South Dakota	695
4	Montana	597
5	Idaho	479
6	Wyoming	408
7	Nebraska	357
8	Tennessee	350
9	Utah	347
10	Rhode Island	301
11	Illinois	298
12	Alaska	291
13	Iowa	265
14	Minnesota	262
15	Kansas	259
16	Oklahoma	256
17	New Mexico	246
18	Indiana	232
19	Missouri	230
20	Arkansas	223
21	Michigan	223
22	Kentucky	216
23	Puerto Rico	208
24	Nevada	198
25	Alabama	194
26	Colorado	183
27	Texas	182
28	North Carolina	163
29	Ohio	154
30	Connecticut	151
31	Mississippi	147
32	South Carolina	147
33	Louisiana	146
34	West Virginia	133
35	Delaware	131
36	New Jersey	129
37	Florida	122
38	Massachusetts	122
39	Arizona	115
40	Pennsylvania	112
41	Georgia	105
42	Virginia	98
43	Maryland	97
44	California	91
45	Washington	78
46	Oregon	65
47	New York	61
48	District of Columbia	52
49	New Hampshire	49
50	Hawaii	47
51	Maine	18
52	Vermont	16

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,826
2	New York	1,696
3	Massachusetts	1,415
4	Connecticut	1,278
5	Louisiana	1,241
6	Rhode Island	1,098
7	Mississippi	1,075
8	District of Columbia	909
9	Arizona	801
10	Illinois	754
11	Florida	749
12	Michigan	739
13	South Carolina	717
14	Georgia	705
15	Delaware	685
16	Pennsylvania	671
17	Maryland	669
18	Texas	608
19	Indiana	595
20	Alabama	572
21	Arkansas	572
22	Nevada	561
23	North Dakota	547
24	Iowa	499
25	New Mexico	449
26	Missouri	434
27	Ohio	434
28	California	431
29	Tennessee	428
30	Virginia	408
31	Minnesota	407
32	Colorado	384
33	North Carolina	383
34	South Dakota	373
35	New Hampshire	344
36	Kentucky	314
37	Washington	312
38	Kansas	309
39	Idaho	302
40	Oklahoma	300
41	Nebraska	297
42	Wisconsin	285
43	Puerto Rico	240
44	Montana	235
45	West Virginia	227
46	Utah	172
47	Oregon	150
48	Hawaii	132
49	Maine	108
50	Wyoming	105
51	Vermont	92
52	Alaska	86

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Iowa	5
2	North Dakota	5
3	South Dakota	5
4	Arkansas	4
5	Indiana	4
6	Kansas	4
7	Wisconsin	4
8	Louisiana	3
9	Mississippi	3
10	Montana	3
11	Nebraska	3
12	Rhode Island	3
13	South Carolina	3
14	Florida	2
15	Georgia	2
16	Illinois	2
17	Kentucky	2
18	Michigan	2
19	Nevada	2
20	New Mexico	2
21	North Carolina	2
22	Tennessee	2
23	Virginia	2
24	Wyoming	2
25	Alabama	1
26	Connecticut	1
27	Delaware	1
28	Idaho	1
29	Massachusetts	1
30	Minnesota	1
31	Missouri	1
32	Oklahoma	1
33	Oregon	1
34	Pennsylvania	1
35	Texas	1
36	Utah	1
37	Washington	1
38	West Virginia	1
39	Alaska	0
40	Arizona	0
41	California	0
42	Colorado	0
43	District of Columbia	0
44	Hawaii	0
45	Maine	0
46	Maryland	0
47	New Hampshire	0
48	New Jersey	0
49	New York	0
50	Ohio	0
51	Puerto Rico	0
52	Vermont	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Lincoln	Arkansas	170,608	1	99
Chattahoochee	Georgia	158,430	2	99
Trousdale	Tennessee	154,466	3	99
Lafayette	Florida	149,964	4	99
Lake	Tennessee	138,255	5	99
Davidson	Tennessee	46,388	254	91
Richland	South Carolina	40,237	396	87
York	South Carolina	22,439	1430	54
Orange	California	18,632	1757	44
Pierce	Washington	11,566	2440	22

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	5,203	1	99
Emporia city	Virginia	5,051	2	99
Kenedy	Texas	4,950	3	99
Galax city	Virginia	4,727	4	99
Randolph	Georgia	4,279	5	99
Richland	South Carolina	633	800	74
Davidson	Tennessee	470	1141	63
Orange	California	445	1192	62
York	South Carolina	342	1487	52
Pierce	Washington	267	1724	45

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.